https://socictopen.socict.org/files/original/b3aa896fbbb991d0dd332bbd4c5379f2.pdf 221ab4fabad0d6961227bcb17eed9875 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Forecasting epidemic spread of SARS-CoV-2 using ARIMA model (Case study: Iran) Creator An entity primarily responsible for making the resource T.T. Tran, L.T. Pham, Q.X. Ngo Description An account of the resource Currently, the pandemic caused by a novel coronavirus, namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the most serious issues worldwide. SARS-CoV-2 was first observed in Wuhan, China, on December 31, 2019; this disease has been rapidly spreading worldwide. Iran was the first Middle East country to report a coronavirus death, it has been severely affected. Therefore, it is crucial to forecast the pandemic spread in Iran. This study aims to develop a prediction model for the daily total confirmed cases, total confirmed new cases, total deaths, total new deaths, growth rate in confirmed cases, and growth rate in deaths. The model utilizes SARS-CoV-2 daily data, which are mainly collected from the official website of the European Centre for Disease Prevention and Control from February 20 to May 04, 2020 and other appropriated references. Autoregressive integrated moving average (ARIMA) is employed to forecast the trend of the pandemic spread. The ARIMA model predicts that Iran can easily exhibit an increase in the daily total confirmed cases and the total deaths, while the daily total confirmed new cases, total new deaths, and growth rate in confirmed cases/deaths becomes stable in the near future. This study predicts that Iran can control the SARS-CoV-2 disease in the near future. The ARIMA model can rapidly aid in forecasting patients and rendering a better preparedness plan in Iran. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Prediction, Iran, epidemic, Autoregressive Integrated Moving Average (ARIMA), covid-19 coronavirus Identifier An unambiguous reference to the resource within a given context DOI: 10.22034/GJESM.2019.06.SI.01 Source A related resource from which the described resource is derived Global Journal of Environmental Science and Management Publisher An entity responsible for making the resource available Global Journal of Environmental Science and Management Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Environmental sciences